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Intrusion Detection Systems Based on Stacking Ensemble learning in VANET

Intrusion Detection Systems Based on Stacking Ensemble Learn...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Mahshid Behravan Ning Zhang Arunita Jaekel Marc Kneppers University of Windsor Windsor ON Canada Telus Communications Inc. Canada

ISBN: (纸本)9781665482387

Vehicular ad-hoc network (VANET) will play an important role in improving driving safety and efficiency in transport systems. As various attacks arise in VANET, it is essential to design mechanisms that can detect these attacks and then mitigate them. In this paper, we make an effort to detect five different position falsification attacks in VANET, including constant attack, constant offset attack, random attack, random offset attack, and eventual stop attack. Two detection systems based on ensemble machine learning algorithms, including stacking ensemble learning algorithms for classification and stacking ensemble learning for neural network, are proposed. We extracted the most important features by performing feature importance techniques. then, we train the proposed learning algorithms on VeReMi dataset which includes five different position falsification attacks with three traffic densities and three attacker densities. Extensive experimental results are provided to evaluate the proposed solutions' effectiveness. Based on our results, stacking ensemble learning for classification algorithm can achieve the best performance in terms of accuracy and recall. In low density traffic, accuracy and recall of stacking ensemble learning for classification algorithms are 1 for the constant attack, constant offset attack, and random attack. Accuracy and recall for the random offset attack are 0.999 and 0.996, respectively. For the eventual stop attack, accuracy and recall are 0.995 and 0.985, respectively. In medium density, accuracy and recall of stacking ensemble learning also achieve the best performance.

关键词： machine learning algorithms Stacking Neural networks Vehicular ad hoc networks Intrusion detection signal processing algorithms Feature extraction

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Hindi Speech-Based Healthcare Chatbot 5th

Hindi Speech-Based Healthcare Chatbot

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5th international conference on Smart Computing and Informatics, SCI 2021

作者： Sinha, Prasenjeet Bafna, Prafulla B. Saini, Jatinderkumar R. Maharashtra Pune India

ISBN: (纸本)9789811697043

Health care is critical to lead a healthy life. However, in cases of unhealthy conditions, consulting with the doctor is very difficult. the proposed idea creates a medical or healthcare chatbot using machine learning (ML) and Natural Language processing (NLP). Speech recognition API and Google Translate API are used to recognize the user’s voice and text translation, respectively. Along with NLP, text classification algorithms facilitate fast, cost-effective and scalable solutions. ML algorithms are implemented on the corpus for prediction and classification. Text data classification is essential in order to minimize manual work and spend time reading the report. In this paper, a corpus of nearly 5000 documents is classified based on the symptoms and using five machine learning algorithms. the misclassification error is used in contrast with technical progress. Out of the six experimented algorithms, Gaussian Naïve Bayes classifier shows the highest accuracy score and out performs all others. the combination of sentence similarity and keyword matching algorithms are used to extract appropriate answers to the particular class and display it to the user. In general, users are not conscious of all the medications or the signs and symptoms of the actual condition. For minor issues, users have to take a time-consuming inspection directly at the hospital. A survey states that around 850 million of 1.3 billion people speak Hindi in India. the proposed chatbot is for these people who can only speak or understand the Hindi language. the speech-based healthcare chatbot algorithms are designed with query interpretation and user message comprehension. the machine learning algorithm is implemented to predict the class and with the combination of sentence similarity and keyword matching algorithms to provide the user with relevant answers. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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Delay Root Cause Analysis and 3D Modeling of LTE Control Communication Using machine learning

Delay Root Cause Analysis and 3D Modeling of LTE Control Com...

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2022 international conference on Artificial Intelligence of things, ICAIoT 2022

作者： Mohammed, Sameer Qutaiba Ilyas, Muhammad Altinbas University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350396768

this study investigates and evaluates delay root cause analysis and 3D modeling of LTE control communication utilizing sophisticated machine learning for network testing. the research studied LTE protocols for 5th-generation mobile telephony and provided guidelines for controlling LTE frequency for background knowledge, although it used an independent technique that did not employ LTE standards. 512 elements of input-output MIMO were employed for 100-GHz and 128 elements for mid-band sub-6-GHz. LOS is always 0.5. this paper is about LTE, not 3D modeling of LTE control path loss type communication using machine learning. this work's route loss depends on cross-pol beam LTE polarization (±45o). the receiver (Rx) operations and transmitter (Tx) activities in the estimated distance of 0.5 km at an approximate altitude of 15.25 m. Distance, handover authentication, rain, atmosphere, and sub-6GHz vs 100GHz weather conditions affect path loss. the methodology has enhanced the spatial variety by boosting transmitting power and transmitting efficiency. Authorizing and sanctioning ANN-based LTE frequency for both mid-band sub-6-GHz and 100-GHz is possible due to its planning and development using open-source material and strategy with high transmission power and rate under doubtful handover confirmation using MIMO input/yield receiving wires. this theory examines LTE innovation dimensioning as unbiased for various handover verification and allows input boundary alterations for various organization arrangement setups for LTE recurrent data transmission from 6 GHz to 100 GHz for three climate sorts. this cycle should be seen as an undeniable level way to examine LTE networks under various air conditions. Using signal handling tool compartment and explicit AI-based ANN calculation from AI toolkit in MATLAB R2019a, it is possible to create a result answer for three climate types in a dataset with an LTE communication level of exactness of downpour assimilation and abundance folia

关键词： 3D modeling

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machine learning Approach on Photoplethysmogram Morphology for Psychiatric Disorders Prediction

Machine Learning Approach on Photoplethysmogram Morphology f...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Azwani Awang Nazrul Anuar Nayan Nik Ruzyanei Nik Jaafar Mohd Zubir Suboh Khairul Anuar A Rahman Siti Nor Ashikin Ismail Dept. of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia UKM Bangi Malaysia Dept. of Psychiatry Hospital Canselor Tuanku Muhriz WP Kuala Lumpur Malaysia Dept. of Electronics & Computer Engineering Technology Universiti Tekninal Malaysia Melaka Malacca Malaysia

ISBN: (纸本)9781665482387

Psychiatric disorders (PDs) interfere with one's functioning and greatly affect a person's quality of life. Prompt diagnosis and intervention at the early stages of these illnesses are important. However, most people are oblivious or unaware of their mental health status as the symptoms may not be easily recognizable. Consequently, complications occur later in life. In this study, a machine learning (ML) approach that distinguishes between case (PD-diagnosed patients) and control (healthy) groups was developed using photoplethysmogram (PPG) morphology. 92 subjects with gender and age-matched PPG data were collected during two phases; baseline and stimulus state of a 10-min experiment. 60 features from PPG morphology were extracted from each phase, and another 30 were obtained from differences between the two phases. A total of 27 out of 90 features exhibited a significant difference. Twelve features extracted by heatmap based on the correlation analysis were fed to five types of ML algorithms: discrimination analysis, k-nearest neighbor, decision tree, support vector machine, and artificial neural network (ANN). the results showed the best performance of 92.86%, 100.00%, and 96.43% for sensitivity, specificity, and accuracy by ANN. thus, a PD prediction model was developed using machine learning techniques from PPG morphology extraction.

关键词： Support vector machines Sensitivity Biological system modeling Morphology signal processing algorithms Artificial neural networks machine learning

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Breast Cancerous Tumor Detection Using Supervised machine learning Techniques 5th

Breast Cancerous Tumor Detection Using Supervised Machine Le...

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5th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Parekh, Mayank V. Rathod, Dushyantsinh Lecturer IT Department Government Polytechnic & PhD Scholar Gujarat Technological University Gujarat Ahmedabad India Associate Professor CE Department Sankalchand Patel College of Engineering Sankalchand Patel University Gujarat Visnagar India

ISBN: (纸本)9789811641763

Around the globe the leading cause of cancer mortality in women is Breast Cancer;BCs, have been under many studies and several analyses have shown that many abnormal conditions and risk of BCs can be diagnosed with the help of fast and perfect judgment of the clinical physicians of this domain. this kind of intelligent decision making is not the thing where every person is good at so here, we have kept Artificial intelligence to use with supporting steps like machine learning Classifiers from Wisconsin Breast Cancer Dataset;WBCD. In the Combination of Human and Computer Intelligence, we have worked our way around the hybrid Mechanism where we can make the use of both entities and detect as well as prevent the risks of Very Early Stages of BCs. Our Mechanism includes the use of Principle Component Analysis;PCA, at the first stage of pre-processing the decreasing features in the pre-processed data the second stage of pre-processing was more beneficial for accessing the multi pre-processed data for analysis and diagnosis. When the Multi Pre-processed data is accessed by the Support Vector machine;SVM, the old Accuracy, Specificity and Significance level was ranging from 95 to 100% but the new levels after the multi pre-processing were decreased to 5% and the improvement was considerable and confirmed that the proposed Algorithm has achieved the optimized results then RF, KNN, DT. It means the Proposed algorithm can assist the human factor with faster decision making and can categorize data (High Risk) malignant and (Low Risk) Benign because to avoid Human errors AI is useful and can achieve optimal results. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Discussion on the Effect of Classroom Concept learning Based on BERT Text Classification

Discussion on the Effect of Classroom Concept Learning Based...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Xiaoyu Tang Xiaoning Huang Xi Lin School of Physics and Telecommunication Engineering Xingzhi College South China Normal University Guangzhou China School of Data Science and Engineering Xingzhi College South China Normal University Shanwei China

In the process of concept learning, students will gradually construct concepts and eventually form a profound and complete concept system. Analyzing what students discuss in class can help teachers effectively understand students' level of conceptual learning and contribute to the development of teaching evaluation level. In this paper, we analyze students' conceptual learning levels by introducing the BERT combination model in deep learning. the research steps mainly include the introduction and formulation of concept learning classification metrics, the collection and preprocessing of datasets, and the construction of combinatorial optimization based on BERT ***, the BERT-RCNN model achieved the best results, with an precision of 83.33%, a recall of 83.34%, and an F1-score of 83.34%.

关键词： Measurement Deep learning Analytical models Bit error rate Text categorization Education signal processing

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Lung Sound Denoising with Adaptive Noise Cancellation of Heart Sounds on a Raspberry Pi-Powered Stethoscope

Lung Sound Denoising with Adaptive Noise Cancellation of Hea...

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international conference on Biomedical Engineering (IBIOMED)

作者： Dharren Sandhi Goutama Aulia A. Iskandar Rusman Rusyadi Department of Biomedical Engineering (Swiss German University) Tangerang Indonesia Department of Mechatronics (Swiss German University) Tangerang Indonesia

ISBN: (数字)9798350389265

ISBN: (纸本)9798350389272

Lung auscultation offers a quick and non-invasive diagnosis of respiratory conditions. However, background and heart noises may influence lung sound clarity and may reduce diagnosis accuracy. Heart noises are particularly challenging to separate from lung sounds due to overlapping frequencies. therefore, this research proposes a method of reducing heart sounds through an LMS adaptive noise cancellation filter with Hilbert Transform and peak thresholding to estimate a reference heart sound signal, within a Raspberry Pi 4B-based digital stethoscope. the lung sound denoising algorithm starts with a Type 1 Chebyshev Bandpass Filter for background noise reduction, followed by wavelet threshold denoising using SureShrink adaptive thresholding, for finer noise reduction. the LMS adaptive noise cancellation filter was then applied to reduce heart sounds and ending with applying the same bandpass filter to smoothen the signal. Performance evaluation of the lung sound denoising algorithm demonstrated significant lung sound clarity improvement, with most of the background noises greatly reduced, achieving an average heart peak reduction of 87.5 % , a good LMS filtering performance with a Pearson correlation coefficient of 98.9% between estimated and replicated reference signals and a large reduction in magnitude within the heart sound frequency range was observed in an FFT. the system's robust signal processing capabilities make it a promising tool for a more accurate respiratory diagnosis and has great future potential to include machine learning to diagnose respiratory diseases.

关键词： Heart Band-pass filters Lungs Noise reduction Adaptive filters signal processing algorithms Stethoscope machine learning Noise cancellation Recording

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Research on machine learning Classification of Mild Traumatic Brain Injury Patients Using Resting-State Functional Connectivity 6th

Research on Machine Learning Classification of Mild Traumati...

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6th international conference on Cognitive Systems and signal processing, ICCSIP 2021

作者： Li, YuXiang Shen, Hui Xie, Hongwei Hu, Dewen College of Intelligence Science and Technology National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9789811692468

In recent years, the incidence of mild traumatic brain injury (mTBI) has been increasing, especially in the military because of soldiers’ special working environment. Current diagnostic systems relying too much on patients’ self-description leads to improper diagnosis and treatment. Finding biomarkers of mTBI becomes significant for diagnostic accuracy improvement. We used machine learning methods to extract highly discriminative functional connectivity features and to discriminate mTBI patients from healthy controls. 31 mTBI patients and 31 healthy controls with matching age, gender and education level underwent resting-state functional magnetic resonance imaging. A promising classification accuracy of 75.81% was achieved using resting-state functional connectivity as features. Moreover, some functional connectivities between certain brain regions of the cerebellum and the sensorimotor were found to exhibit the highest discriminative power, which might provide a new idea for the discovery of stable biomarkers of mTBI. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Biomarkers

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Pix2Pix Generative adversarial Networks (GAN) for breast cancer detection

Pix2Pix Generative adversarial Networks (GAN) for breast can...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Zainab Aizaz Kavita Khare Afreen Khursheed Aizaz Tirmizi Department of Electronics and Communication Engg. Maulana Azad National Institute of Technology (MANIT) Bhopal India Department of Electronics and Communication Engg. Indian Institute of Information Technology (IIIT) Bhopal India Institute of Advanced Skill Training (IAST) Bhopal India

ISBN: (纸本)9781665476485

Breast cancer remains to be one of the most common cancers throughout the world. Detection of mammographically occult breast cancer is a trivial process due to hidden tumours in women with dense breasts. Data augmentation techniques are in demand due to existing paucity in the dash images that are labeled. In this brief, a model for the detection of mammographically occult breast cancer is developed with pix2pix Generative Adversarial Networks (GAN) as in imperative tool for data augmentation. In this work, GANs are used to construct transformations of original images in the dataset. the U-net based of CNN layers is used to preserve long-range connections in wide neighbourhoods to provide a balanced partitioning. For the cancer detection, this model can be used as an individual layer in a Convolutional neural network (CNN). Upon comparison with transfer learning approach, the proposed approach has higher accuracy for two VGG-16 and Resnet-50 CNNs.

关键词： machine learning algorithms Image analysis Transfer learning signal processing algorithms signal processing Generative adversarial networks Cancer detection

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Breast Cancer Detection and Prediction using Federated Multicriteria machine learning

Breast Cancer Detection and Prediction using Federated Multi...

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international conference on signal processing and Information Security (ICSPIS)

作者： Marco Repetto Davide La Torre Digital Industries Siemens Italy Milan Italy Department of Economics Management and Statistics University of Milano-Bicocca Milan Italy SKEMA Business School Université Côte d'Azur Sophia-Antipolis France Department of Oncology University of Milan Milan Italy

ISBN: (数字)9781665492652

ISBN: (纸本)9781665492669

Breast cancer is still one of the most common cancers in women, and it is also the leading cause of mortality among women. Breast cancer detection has been improved using a variety of machine learning and Deep learning techniques. Federated learning is a distributed learning technique that allows models to be trained on a vast set of decentralized data. In this paper we present a new Multiple Criteria Optimization approach for Federated learning. We use Goal Programming in particular to address the Federated learning problem's diverse set of objectives. Numerical experiments also demonstrate the efficacy of this strategy in terms of performance.

关键词： Training Resistance Privacy Federated learning signal processing Programming Predictive models

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